Risk factor identification is the basis for risk assessment. To quantify the safety risks of unmanned vessels in inland rivers, through analysis of previous studies, the safety risk impact factor framework of unmanned vessels in inland rivers is established based on three aspects: the ship aspect, the environmental aspect, and the management and control aspect. Relying on Yangtze River, a fuzzy Bayesian network of the sailing safety risk of unmanned ships in inland rivers is constructed. The proposed safety risk model has considered different operational and environmental factors that affect shipping operations. Based on the fuzzy set theory, historical data, and expert judgments and on previous works are used to estimate the base value (prior values) of various risk factors. The case study assessed the safety risk probabilities of unmanned vessels in Yangtze River. By running uncertainty and sensitivity analyses of the model, a significant change in the likelihood of the occurrence of safety risk is identified, and suggests a dominant factor in risk causation. The research results can provide effective information for analyzing the current safety status for navigation systems of unmanned ships in inland rivers. The estimated safety risk provides early warning to take appropriate preventive and mitigative measures to enhance the overall safety of shipping operations.

Real-time kinematic (RTK) positioning technology utilizes carrier phase measurements for receiver positioning and requires resolving integer carrier phase ambiguities to achieve high-precision results. This process, known as ambiguity resolution (AR), traditionally relies on the classical LAMBDA algorithm and partial ambiguity resolution (PAR) strategy. However, these established methods are limited in processing high-dimensional data across multiple frequencies and systems. Therefore, this study aims to investigate the challenges posed by these methods in such scenarios and propose an alternative approach. The proposed method, called MPARELAM, integrates the enhanced LAMBDA (ELAMBDA) algorithm with a modified PAR (MPAR) strategy, combining advanced search capabilities and robust ambiguity resolution. The quantitative evaluation of the MPARELAM method considers the success rate and the total time of AR, as well as the average standard deviation of the positioning results. The results reveal that the proposed method significantly enhances AR accuracy and reduces positioning errors in carrier-phase-based RTK positioning.

This paper introduces a novel approach in the field of tightly coupled GNSS/INS integration, aimed at substantially reducing positioning errors. We propose an innovative adaptive robust extended Kalman filter (AREKF), termed MAREKF, which utilizes the Mahalanobis distance (MD) to effectively mitigate the challenges posed by strong system nonlinearity and modeling errors, thereby enhancing vehicle positioning accuracy. Traditional AREKF methods, including the proposed MAREKF, often struggle with the identification of gross errors in measurement data and may exhibit imbalances in parameter adjustments. To overcome these challenges, we propose the non-holonomic constraint (NHC)-driven MAREKF (NMAREKF), integrating non-holonomic constraint conditions to enhance adaptability and robustness in nonlinear environments. The NMAREKF shows a significant improvement in filtering process and system stability. The empirical evaluation, using datasets from diverse environments, focuses on performance metrics such as root mean square error and average standard deviation of position, velocity, and attitude. The results demonstrate that NMAREKF outperforms existing methods in handling kinematic and measurement model errors. The NMAREKF method, blending NHC and MD, emerges as a promising solution for reducing positioning errors in complex navigation scenarios, offering significant theoretical and practical advancements in the field of precision positioning.

Feature correspondence selection, which aims to seek as many true matches (i.e., inliers) as possible from a given putative set while minimizing false matches (i.e., outliers), is crucial to many feature-matching based tasks in computer vision. It remains a challenging problem how to deal with putative sets with low inlier ratios. To address this problem, in this paper, we propose a novel correspondence selection strategy, which is guided by Grid-based Motion Statistics (GMS). We first adopt the GMS to generate a small correspondence set with a high inlier ratio. Then, an accurate geometric model is built using the above correspondence set. Finally, the built geometric model is used to filter the given putative correspondence set to obtain true correspondences. The experimental results on benchmark datasets demonstrate that our proposed approach outperforms the state-of-the-art approaches for putative sets with various inlier ratios, especially for cases with low inlier ratios.

The traditional distributed WSNs fire remote monitoring system has single monitoring variables and incomplete fire detection data, which leads to large monitoring error and long delay. A distributed WSNs fire remote monitoring system based on fuzzy algorithm is designed. The hardware part of the system consists of distributed WSNs fire remote monitor, air temperature and humidity parameters acquisition, LCD unit and system power supply unit. The remote fire monitor is designed by using microprocessor C8051F060, and the centralized monitoring of information is realized by using distributed WSNs. Based on this, the fuzzy algorithm is introduced to standardize the fire detection data, and the fuzzy similar matrix is established. According to the improved similarity coefficient, the matrix is solved, the fuzzy equivalent matrix is calculated, and the optimal threshold value of fuzzy monitoring is determined. The fuzzy language monitoring rules are set by using three fuzzy variables of current, temperature and smoke to complete the design of distributed WSNs fire remote monitoring system. The simulation results show that: compared with the traditional fire monitoring system, the system designed in this paper has higher throughput limit, shorter delay, and the accuracy rate of monitoring and alarm is higher than 95%. The experimental results show that the system has good generalization ability and is suitable for large-scale high-rise buildings and large-scale networks.